For the evaluation of wind energy potential, probability density functions (pdfs) are usually used to describe wind speed distributions. The selection of the appropriate pdf reduces the wind power estimation error. The most widely used pdf for wind energy applications is the 2-parameter Weibull probability density function. In this study, a selection of pdfs are used to model hourly wind speed data recorded at 9 stations in the United Arab Emirates (UAE). Models used include parametric models, mixture models and one non-parametric model using the kernel density concept. A detailed comparison between these three approaches is carried out in the present work. The suitability of a distribution to fit the wind speed data is evaluated based on the log-likelihood, the coefficient of determination R2, the Chi-square statistic and the Kolmogorov–Smirnov statistic. Results indicate that, among the one-component parametric distributions, the Kappa and Generalized Gamma distributions provide generally the best fit to the wind speed data at all heights and for all stations. The Weibull was identified as the best 2-parameter distribution and performs better than some 3-parameter distributions such as the Generalized Extreme Value and 3-parameter Lognormal. For stations presenting a bimodal wind speed regime, mixture models or non-parametric models were found to be necessary to model adequately wind speeds. The two-component mixture distributions give a very good fit and are generally superior to non-parametric distributions

The quantile mapping method is a bias correction method that leads to a good performance in terms of precipitation. Selecting an appropriate probability distribution model is essential for the successful implementation of quantile mapping. Probability distribution models with two shape parameters have proved that they are fit for precipitation modeling because of their flexibility. Hence, the application of a two-shape parameter distribution will improve the performance of the quantile mapping method in the bias correction of precipitation data. In this study, the applicability and appropriateness of two-shape parameter distribution models are examined in quantile mapping, for a bias correction of simulated precipitation data from a climate model under a climate change scenario. Additionally, the impacts of distribution selection on the frequency analysis of future extreme precipitation from climate are investigated. Generalized Lindley, Burr XII, and Kappa distributions are used, and their fits and appropriateness are compared to those of conventional distributions in a case study. Applications of two-shape parameter distributions do lead to better performances in reproducing the statistical characteristics of observed precipitation, compared to those of conventional distributions. The Kappa distribution is considered the best distribution model, as it can reproduce reliable spatial dependences of the quantile corresponding to a 100-year return period, unlike the gamma distribution.

Time series forecasting of meteorological variables such as daily temperature has recently drawn considerable attention from researchers to address the limitations of traditional forecasting models. However, a middle-range (e.g., 5–20 days) forecasting is an extremely challenging task to get reliable forecasting results from a dynamical weather model. Nevertheless, it is challenging to develop and select an accurate time-series prediction model because it involves training various distinct models to find the best among them. In addition, selecting an optimum topology for the selected models is important too. The accurate forecasting of maximum temperature plays a vital role in human life as well as many sectors such as agriculture and industry. The increase in temperature will deteriorate the highland urban heat, especially in summer, and have a significant influence on people’s health. We applied meta-learning principles to optimize the deep learning network structure for hyperparameter optimization. In particular, the genetic algorithm (GA) for meta-learning was used to select the optimum architecture for the network used. The dataset was used to train and test three different models, namely the artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory (LSTM). Our results demonstrate that the hybrid model of an LSTM network and GA outperforms other models for the long lead time forecasting. Specifically, LSTM forecasts have superiority over RNN and ANN for 15-day-ahead in summer with the root mean square error (RMSE) value of 2.719 (°C).